System and method of improving image quality in digital image scanning and printing by reducing noise in output image data

ABSTRACT

A system and method of reducing noise in output image data is provided. Grayscale image data having a plurality of pixels is received and processed. During processing, pixels which may produce noise are identified, and a mask associated with the image data is generated. The mask provides information related to the pixels, such as opaque and transparent regions for overlaying the pixels. The image data and the mask are compressed and stored. The mask assists in preventing the identified pixels from being visible when the image data is output, thereby reducing the noise in the image.

BACKGROUND

1. Field

The present disclosure is generally related to a method of improvingimage quality in digital printing or scanning. More specifically, thepresent disclosure relates to a method of reducing noise in output imagedata manipulated by segmentation processes such as mixed-raster content(MRC).

2. Description of Related Art

Image data comprises a number of pixels, each pixel corresponding to adefined location in the image. The pixels have a number of componentsthat contribute to defining the image, such as color and intensity. Theimage data generally includes various color or gray levels, whichcontribute to the intensity of each pixel in the image. Each pixel ofthe image is assigned a number representing the amount of light or graylevel for that space at that particular spot; i.e., the shade of gray inthe pixel. Binary image data has two possible values for each pixel,black (represented by the number “1”) or white (represented by thenumber “0”). Images that have a large range of shades are referred to asgrayscale images. For example, grayscale images have an 8-bit value (orhigher) per pixel comprising 256 tones or shades of gray for each pixelin the image (gray level of 0 to 255). Grayscale image data may also bereferred to as continuous tone or contone image data. In some instances,it is possible to create the impression of a continuous tone image byusing a process such as halftoning, such that the image data isconverted and “appears” to be a continuous tone image. The halftoneprocess is generally known, and various methods for halftoning exist.

The intensity of a pixel is expressed within a given range, between aminimum and a maximum (inclusive), from 0 (total presence of color orgray level, i.e., white) and 1 (total absence of color or gray level,black), with any fractional values in between. When outputting imagedata to an output device (e.g., copier, printer, or multi-functiondevice (MFD)), a percentage scale may be used to identify how much inkis employed for a print job. For example, when printing in halftone, theamount of ink or toner supplied may be between 0% (or none)(i.e., purewhite) and 100% (i.e., solid black), inclusive.

Additionally, before image data is output to a digital output device,the image data must be compressed, i.e., coded to minimize the spaceneeded to store and output the image data. In particular, due its largesize, digital image data that is output to a device such as amulti-function printer (e.g., for copying and/or printing) demands thatimages be compressed. For color or grayscale image data that is to becompressed, conversion of such image data to binary image data isgenerally not sufficient.

Generally, the compression process of image data may be referred to as“lossless” (also referred to as “reversible” or “noiseless”) if thereconstructed image is substantially identical to the original.Alternatively, if the reconstructed image is not identical to theoriginal (i.e., if the reconstructed image is of inferior quality,though not necessarily detected visually), the compression process maybe referred to as “lossy” compression. Because lossless compressionoften does not yield a file size that is small enough for output devicesor systems (e.g., copy and print systems), lossy compression istypically used.

One known popular technique for manipulating and compressing digitalimage data using lossy compression is a standard known as JPEG imageformat. However, when images are compressed using JPEG format to asmaller file size, the quality of the image data can be severelydegraded. For example, color or grayscale image data may be compressedusing JPEG compression techniques; however, this type of compressionusually produces noise (or fringe) around or near the color parts of anoutput image. Additionally, though lossy methods such as JPEG mayprovide acceptable compression for varying contone or grayscale imagedata, lossy methods tend not to work well on binary image datacomprising sharp edges or transitions, thus also creating noise. Inparticular, JPEG compression tends to produce noise around edges oflines and text in image objects. To reduce the noise for image datacompressed in JPEG format, one current solution is to reduce the amountof compression of the image data. However, reducing the amount ofcompression typically produces an undesirable result of a larger filesize.

An alternative technique for manipulating digital image data includessegmentation (or auto-segmentation). Generally, auto-segmentationtechniques are known, and are used to select the most appropriate methodto render the various object types (e.g., black and white or colorimages, text, photos, etc.) present in an image. In some segmentationtechniques, separation modules are used to detect and separate text fromlike halftone parts of image data so that text image data may compresseddifferently as compared to halftone image data. For example, it isgenerally known in the art that a format such as mixed raster content(MRC) (also referred to as multiple raster content) may be used tomanipulate image data. However, such segmentation may be quite complex,as the types of data or objects in the image data must be determined,then separated or divided into segments or planes, and compressedaccording to the appropriate technique or method.

Therefore, a simpler, yet effective method for compressing and storingimage data using segmentation techniques such as MRC, while stillreducing noise and the like in the digitally output image, is desirable.

SUMMARY

One aspect of the present disclosure provides a method of reducing noisein output image data. The method includes: receiving grayscale imagedata having a plurality of pixels; processing each pixel to identifypixels which produce noise in an output image; and generating a maskassociated with the grayscale image data, the mask providing informationrelated to the pixels of the image data. The image data and the mask arethen compressed and stored. The mask that is generated assists inpreventing the identified pixels of the image data from being visiblewhen the image data is output, thereby reducing the noise in the outputimage.

An aspect of the present disclosure provides an image data processingsystem. The system includes an input device configured to receivegrayscale image data having a plurality of pixels. A processor isconfigured to process each pixel to identify pixels which produce noisein an output image. A mask generator is configured to generate a maskassociated with the grayscale image data. The generated mask providesinformation relating to the pixels of the image data. A compressor isprovided to compress the image data and the mask, and a storage devicestores the image data and the mask. The mask assists in preventing theidentified pixels of the image data from being visible when the imagedata is output, thereby reducing the noise in the output image.

Other features of the present disclosure will become apparent from thefollowing detailed description, the accompanying drawings, and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application with color drawingwill be provided by the Office upon request and payment of the necessaryfee.

FIG. 1 illustrates an exemplary embodiment of a segmentation techniquefor compressing image data;

FIG. 2 illustrates an illustrative embodiment of a segmentationtechnique for compressing image data in accordance with an embodiment ofthe present disclosure;

FIG. 3 illustrates a flow chart diagram illustrating a method ofreducing noise in image data in accordance with an embodiment of thepresent disclosure;

FIGS. 4, 5, and 6 illustrate detailed views of image data, a mask, andoutput image data, respectively, in accordance with an embodiment of thepresent disclosure;

FIG. 7 illustrates an exemplary embodiment of output image data that isproduced using known methods;

FIG. 8 illustrates an exemplary embodiment of output image data that isproduced in accordance with an embodiment of the present disclosure;

FIG. 9 illustrates an exemplary embodiment of output image data that isproduced using known methods;

FIGS. 10 and 11 illustrate two embodiments of output image data that areproduced in accordance with an embodiment of the present disclosure; and

FIG. 12 is a block diagram illustrating an image data processing systemthat may be used to compress image data and generate a mask inaccordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

To enhance image features and reduce noise in image data, the image datamay be manipulated and/or transformed by identifying the compressionneeds of the image data. For example, to manipulate image data havingdifferent compression needs, different formats may be used, such asmixed raster content (MRC). As noted above, MRC format includes imagedata in more than one image plane—an approach to satisfying thecompression needs of differing types of data—which separates image datainto a plurality of planes and separately applies an appropriatecompression technique to each image plane. FIG. 1 illustrates anexemplary embodiment of an MRC format or segmentation technique forcompressing image data representing a page 100 or document. The imagedata of the page 100 may be segmented into a plurality of planesincluding a background plane 12, a foreground plane 14, an associatedselector or mask plane 10, and an optional hint plane 16 (sometimes alsoreferred to as a rendering plane). The hint plane may be used to assistthe rendering of the page 100 to output the content of page 100 todifferent displays or marking devices, for example. As is generallyknown in the art for this type of MRC image format, however, any numberN of foreground color and mask plane pairs 14, 10 may be provided. It isalso generally known that the foreground plane 14 may comprise a singlecolor. Typically, each plane is used for holding or storinginformation/image data related to the page 100 or document. For example,the selector or mask plane 10 may be used to store an image of the textand line drawing information. The text may be provided and stored in abinary format. The background plane 12 may typically be used to storecontone information such as pictures or photos or varying backgroundcolors. The foreground plane 14 may store the color of the text and/orline art. The rendering plane 16 may be used to communicate additionalinformation about the content of the page 100 or document. For example,the rendering plane may include color hints that identify a colormatching strategy or halftone data to use for the various objects on thepage.

Segmenting the image data of the page 100 into different planes such asthose noted above allows for different compression methods to apply toeach of the different planes. For example, one exemplary MRCrepresentation specifies that the foreground plane 14 and backgroundplane 12 be JPEG compressed, and the selector plane 10 be ITU-G4compressed (standard Group 4 facsimile compression). Compression of therendering plane 16 is optional.

The system and method described herein is a simpler case of segmentationor separation formatting techniques that work particularly well withtext and line art image data that is either in color or black and white(or both). This disclosure describes a very low level of segmentationprocessing for compressing, storing, and outputting image data. Such amethod and system provides the ability to store images with a reducedamount of (visible) noise around edges of line and textual image data.This disclosure assists in improving image quality of digitally outputimages, including those having a plurality of different colors.Additional features and advantages of the method and system will becomeevident throughout the description below.

FIG. 2 illustrates a simplified segmentation technique for compressingimage data. For example, the image data of a page 102 may be segmentedinto a plurality of planes including an image plane 20 that iscompressed. Additionally, a mask plane 22 may be provided. Generally,the image plane 20 represents the image data of page 102 that isreceived by an input device. The image data comprises a plurality ofpixels to be compressed, processed and stored and/or output to an outputdevice. The image data may comprise grayscale or contone image data, forexample, and/or binary text or line art image data. For example, theimage data of plane 20 may include line art objects 24 and 26.

Before digital image data is stored and/or output, the image data mustbe compressed. For example, to reduce the size of the image data of page102 (or a document or the like), the image data may be compressed usinga lossy compression technique such as JPEG compression. However,compression techniques such as JPEG compression may produce noise inimage objects. “Noise” is defined as pixels in image data having coloror gray values that are undesirable, and may be introduced as a defectduring the compression or decompression, for example. The noise may beproduced during compression of pixels of image data, and may be mostnoticeable, for example, along the edges of lines and text in imageobjects. Other known terms in the art such as mosquito noise, fringe,ringing artifacts, and the like are hereby included and defined asnoise. Thus, when the image data of a page or document is compressed,the image plane 20 may comprise noise 27 and/or 28 around the edges ofthe lines of text and/or line art objects.

Thus, a mask plane 22 (also referred to throughout this disclosure as“mask”) is generated and associated with the compressed image data ofimage plane 20. A “mask plane” or “mask” comprises information regardingthe pixels of the image data of page 102. Generally, the mask plane 22comprises information to overlay the image plane 20, such that, uponoutputting the image data, the noise 27 and/or 28 of text and/or lineart objects in page 102 is substantially reduced or substantiallyeliminated. Such pixels may be considered as background areas, forexample.

More specifically, the mask 22 may be generated so as to cover the noise27 and/or 28 near the lines or text while leaving the line or textuncovered. For example, the mask may be “transparent” in dark (e.g.,solid black), gray, or color areas of text and lines, and “opaque” inbackground areas (e.g., such as those areas that may be identified inthe background plane 12 of FIG. 1). Throughout the disclosure,“transparent” is defined as an area of the mask plane 22 associated witha pixel that is free or uncovered, while “opaque” refers to an area ofthe mask 22 associated with a pixel that is solid or covered. In anembodiment, opaque or solid areas may be areas that are white in color.The opaque areas may be associated with pixels of the image data thatare determined to produce noise near the text or line objects, such thatthe determined pixels are covered with an overlay. Thus, when the maskplane 22 is associated or applied to the image plane 20, the noise 27and/or 28 is masked out while leaving the lines or text untouched.

In an embodiment, the mask plane 22 may be generated after processingeach pixel (i.e., on a pixel-by-pixel basis) of the input image data;i.e., before any compression is applied to the input image data. In anembodiment, the input image data may be lightly compressed so that nosubstantial data or information (e.g., related to image plane 20) islost and no substantial noise is introduced. In an embodiment, the mask22 may be embedded or linked with the processed image data. In anembodiment, the mask 22 may comprise information similar to processingtechniques such as tagging or mapping. For example, the mask plane 22may associate a tag with a pixel to represent an opaque or a transparentarea of the mask. The methods of generating a mask plane 22 should notbe limiting. Further description with regard to processing pixels ofimage data and generating mask 22 are provided below.

The image data of the image plane 20 is compressed after generating themask plane 22. Generally, any compression technique that is appropriatefor compressing grayscale image data may be used. For example, a JPEGcompression technique may be used. Additionally, the mask plane 22 mayalso be compressed. In an embodiment, the image plane 20 and mask plane22 may be compressed using separate (but appropriate) techniques. Forexample, the image plane 20 may be compressed using a lossy compressiontechnique, while the mask plane 22 may be compressed using a binary orlossless compression technique. An example of a lossless compressiontechnique may be ITU-G4, JBIG or JBIG2 compression. However, any knowntechnique or combination of techniques may be used in the presentmethod, and, therefore, should not be limited to those described herein.

In an embodiment, the image plane 20 and mask 22 are associated andstored together. For example, to store the mask 22 with the image data20, the planes 20 and 22 may be stored using page description languagesuch as Portable Document Format (PDF), or any other suitable pagedescription language formats. However, the image plane 20 and 22 may becompressed using different compression techniques. For example, theimage plane 20 may be compressed using JPEG compression while the maskplane 22 may be compressed using JBIG2 compression.

FIG. 3 illustrates a flow chart diagram illustrating a method 38 ofreducing noise in output image data in accordance with an embodiment. Asshown by 40, grayscale image data comprising a plurality of pixels isreceived. The grayscale image data may be received, for example, by aninput device. As described below with respect to FIG. 12, an inputdevice may be any type of device for inputting image data, such as ascanning device, facsimile device, computing device, etc. Each pixel ofthe grayscale image data is then processed as shown by 42 to identifypixels which may produce noise in an output image. For example, asfurther described below, the pixel being processed may be identified asa noise producing pixel, or the pixel being processed may be identifiedas a pixel to be visible while the pixels neighboring the pixel beingprocessed may be identified to produce noise. For example, as alsofurther described below, a threshold comparison of a pixel's intensityor luminance-information may be used to determine which pixels mayproduce noise. After processing the grayscale image data on apixel-by-pixel basis, a mask to be associated with the image data isgenerated as shown by 44. The mask may comprise information related tothe pixels of the image data. The mask assists in preventing theidentified pixels of the image data from being visible when the imagedata is output, thereby reducing the noise in the output image. Forexample, as noted above, the generated mask may contain information tooverlay the image data and thus mask or substantially reduce oreliminate pixels which are determined to provide noise in an outputimage.

After generating the mask 44, the image data and the mask are compressedas shown by 46 and then stored together as shown by 48. The image dataand mask may be compressed using different compression techniques (e.g.,such as JPEG and JBIG2, as noted above), but stored together. In anembodiment, the mask may be embedded or linked with the compressed imagedata. In an embodiment, the mask may be provided in the form of tags,i.e., by tagging a pixel. For example, applying a tag to a pixel mayrepresent an area of the mask that may be opaque (i.e., covering oroverlaying the pixel). Alternatively, a tag may represent an area of themask that may be transparent (i.e., allowing the pixel to be output). Inan embodiment, the image data and the mask may be stored separately.

In an embodiment, the image data and the associated mask may bedecompressed and output to an output device as represented by 50 and 51.An output device may be any type of device that is designed to outputthe image data and the mask. For example, the output device may display,copy, print, or send the image data. Such an output device may be anMFD, for example. The output device may decompress the image data andthe mask before output. Further description with regard to outputdevices is further provided below with respect to FIG. 12.

In an embodiment, when each pixel is processed 42, the pixel beingprocessed may be identified as a noise producing pixel. For example, asdescribed below, a value of or other information related to a pixel(e.g., intensity, luminance) may be used to determine if that pixelshould be identified as associated with a transparent or opaque area ofthe mask which will be generated. If the pixel being processed isdetermined to be a pixel which produces noise, the pixel may be anidentified pixel. In an embodiment, the pixel being processed may beidentified as a pixel to be visible when the image data is output, andthe identified pixels comprise pixels neighboring the pixel beingprocessed. For example, if a pixel being processed is determined to be apixel which will assist in producing a higher quality output image, theneighboring pixels or pixels in its surrounding environment may beidentified as pixels that will produce noise in an output image.

When each pixel is processed as shown by 42, the intensity of the pixelmay be used as an identification of producing noise in an output image,for example. More specifically, the intensity of a chosen pixel may beprocessed by comparing the intensity of that pixel to a threshold value,such as shown by 52. In an embodiment, if the intensity of the chosenpixel is greater than the threshold, the area of the mask associatedwith that pixel is noted as transparent. If the intensity of the chosenpixel is less than the threshold, the area of the mask associated withthat pixel is noted as opaque. Thus, the method continues on apixel-by-pixel basis comparing the intensity of each pixel to thethreshold value in order to generate a mask, such as represented by masklayer 22 in FIG. 2, to overlay the image data in the output image.

In an embodiment, luminance information or values of the pixels of theinput image data may be used. For example, the luminance information maybe compared to a threshold value to determine if the luminance of achosen pixel is greater than (i.e., darker than) the threshold. In anembodiment, if the luminance of the chosen pixel is greater than thethreshold, the area of the mask associated with that pixel is noted astransparent. If the luminance of the chosen pixel is less than thethreshold, the area of the mask associated with that pixel is noted asopaque. Thus, the method continues on a pixel-by-pixel basis comparingthe luminance of each pixel to the threshold value in order to generatea mask for overlaying the output image data. The property, information,or values associated with pixels that are used to determine transparentand opaque areas for generation of the mask should not be limited tothose mentioned herein.

In an embodiment, tags may associated with pixels of the image data toidentify opaque areas of the mask. For example, if a property (e.g.,intensity, luminance, etc.) indicates that a chosen pixel A is less thana threshold value, pixel A may be tagged, thus indicating that the areaof the mask plane associated with pixel A should be opaque.Alternatively, tags may be used to identify areas of the mask thatshould be transparent. For example, if a property indicates that achosen pixel B is greater than a threshold value, pixel B may be taggedto indicate that the area of the mask plane associated with pixel Bshould be transparent. Alternative processing methods, such as mapping,may also be used to assist in generating the mask plane, and should notbe limiting.

In an embodiment, further processing may be performed on each pixel todetermine if the pixel(s) neighboring the pixel being processed shouldbe identified as noise producing pixels. For example, in one embodiment,if the chosen pixel is compared to the threshold and determined to bewithin a specific range (e.g., having a value or difference within arange of no more than approximately fifteen (15) to approximately twenty(20) percent (%) as compared to the threshold), the chosen pixel may beassociated with a transparent area of the mask while having possibleneighboring pixels to be associated with an opaque area of the mask. Atag may be associated with a chosen pixel, for example, to associateopaque areas of the mask to its neighboring pixels.

In another embodiment, a pixel being processed may be further processedby comparing the chosen pixel to one or more of its neighboring pixelsto determine noise producing pixels. For example, in an embodiment, ifan intensity of a chosen pixel is greater than a threshold, furtherprocessing may be performed to determine if pixel(s) neighboring thepixel being processed should be identified as noise producing pixels. Inone embodiment, a “rate of change” criteria may be used to determine arelationship between a pixel and neighboring pixels. An example of suchcriteria may be that the intensity (or other value or information) ofthe chosen pixel may be then compared with the intensity (or other valueor information) of the neighboring pixels. A second threshold value thatis similar or different to the first threshold value for the chosenpixel may be used. For example, if the comparison of the intensities (orother value or information) or rate of change is less than a threshold(e.g., the difference in intensities between the chosen pixel and atleast one neighboring pixel is less than a threshold, i.e., theneighboring pixel(s) has an intensity that is near the intensity of thechosen pixel), the area of the mask associated with the neighboringpixel(s) may be noted as transparent. Alternatively, a second rangevalue may be used to during the comparison of the pixel with itsneighboring pixel(s). For example, if the comparison or difference inintensity values of neighboring pixel(s) is within a range of no morethan approximately thirty (30) percent (%), the neighboring pixel(s) maybe associated with a transparent area of the mask. In contrast, if thecomparison of the pixel and its neighboring pixels is greater than athreshold (e.g., if the comparison or difference in intensity or valuesis greater than 30%), the area of the mask associated with theneighboring pixel(s) may be noted as opaque.

In another embodiment, neighboring pixels of one or more differentcolors as compared to the chosen pixel to be processed may be identifiedpixels (e.g., to be associated with an opaque area of a mask to begenerated). The above-described processing methods, however, areexemplary only, and any other processing methods or algorithms may beused to process each pixel of the image.

In an embodiment, the threshold value may be predetermined or preset. Inan embodiment, the threshold value may be chosen or determined uponinput of the grayscale image data, as illustrated by 54. For example,the threshold value may be dynamically generated locally or globallyfrom the input image data. A user may also determine the threshold valueto be used, and provide such value by inputting the value into theoutput device.

In another embodiment, the threshold value may be determined by local orglobal statistics of the pixel information of the received grayscaleimage data. For example, after the image data is received or input asshown by 40, an intensity (or luminance) of each pixel of the image datamay be determined as illustrated by 56. After the intensities (orluminances) are determined, a threshold value may be determined 54 basedon the intensity values (or luminance values) of the pixels of the imagedata. In an embodiment, a histogram may be determined locally by aninput device.

As an example, pixels of grayscale images have 256 tones or shades ofgray for each pixel in the image; thus, the gray level or intensityvalue may range from 0 to 255. In an embodiment, a user may choose thethreshold value to be 128. Therefore, any pixel of the image data whoseintensity value is less than 128 will be covered by the mask. In anembodiment, an average or mean intensity value of all of the pixels ofthe image data may be determined. In an embodiment, an average or meanintensity value may be chosen as a threshold value. In anotherembodiment, a median value (i.e., the value separating the higher valuesfrom the lower values) may be chosen as a threshold value. In anembodiment, a mode value (i.e., the intensity value that occurs mostfrequently in the pixels) may be chosen as a threshold value. In yetanother embodiment, the mean, median, or mode intensity values may beused to determine a threshold value.

In an embodiment, the threshold value may be used to determine a secondthreshold value. The threshold value may also be used to determine oneor more ranges. In an embodiment, the threshold value or range may alsobe used to determine a relationship between a pixel and its neighboringpixels (e.g., when comparing values to identify if they are within aspecified range).

Generally, simpler thresholding methods such as those noted above may beused. However, it should be noted that the method of determining thethreshold value should not be limiting. For example, in an embodiment,the thresholding may be supplemented with more complex segmentationmethods or algorithms whereby pixels are inspected and grouped togetherif they meet the threshold criteria and a rate of change criteriawhereby a pixel is sufficiently different from its neighboring pixel.Any of such information may be determined locally, such as by asegmentation module 94 in a system 80, for example, as shown and furtherdescribed with respect to FIG. 12. Examples of segmentation algorithmsused to generate MRC images that may be utilized include U.S. Pat. No.7,242,802, entitled “Segmentation Method and System for Multiple RasterContent (MRC) Representation of Documents,” issued Jul. 10, 2007, andU.S. Pat. No. 7,343,046, entitled “Systems and Methods for OrganizingImage Data into Regions,” issued on Mar. 11, 2008, both of which areassigned to the same assignee of the present disclosure, and both ofwhich are incorporated herein by reference in their entirety. U.S. Pat.No. 7,242,802, for example, describes the generation of a three-layerMRC image with bands of the image data being process. U.S. Pat. No.7,343,046 describes a method wherein pixels of similar color and sharpedges are identified and sorted, thus requiring the fall image to bestored in the memory.

FIGS. 4, 5, and 6 illustrate detailed views of image data, a mask, andoutput image data, respectively, in an exemplary embodiment. FIG. 4illustrates an example of a detailed view of a plurality of pixels ofgrayscale image data 60 that may be input or received and/or compressed(e.g., using JPEG compression). As shown, the pixels comprise aplurality of intensity levels 66, 68, and 70. For example, the pixelsmay be approximately near 100% intensity (or solid color) such as pixels66, approximately near 66% intensity such as pixels 68, or approximatelynear 33% intensity such as pixels 70. As the pixels of the grayscaleimage data 60 are processed, a mask 62 as shown in FIG. 5 may begenerated. As shown, the mask 62 comprises opaque or white areas 72 andtransparent or clear areas 74. The opaque areas 72 of the mask aredesigned to cover those pixels identified as noise, while thetransparent areas 74 allow the remaining pixels to be visible. Forexample, for the image data 60 shown in FIG. 4, the pixels may becompared to a threshold value that may correspond to 60% intensity.Thus, pixels 70 would be covered by the mask 62 when the image data isoutput, as illustrated in FIG. 6.

The method or technique as herein described may improve image quality bycleaning up some of the noise and artifacts resulting from JPEGcompression of grayscale image data (or other lossy compressiontechniques) while producing a comparable or even smaller file size forstorage and output. Also, because the method utilizes a simpler,efficient, and limited segmentation technique, the segmentation defectrelated image quality issues are reduced or eliminated. Generally, anyknown separation or segmentation technique may be used; however, usingthe described simplified segmentation technique with the mask plane(e.g., as illustrated in FIG. 2) provides a simpler algorithm as thereis no need to determine which plane the image data should be dividedinto.

Further, this method only needs to determine whether a pixel is white ornot white. For example, in an embodiment where the image data may besegmented into grayscale image data and not grayscale image data (e.g.,extraction of pixels with similar color or grayscale value), the pixelsof image data have to be separated and compressed in order to generatemasks. Alternatively, in other methods, one may have to determine thedifferent types of colors and/or perform some type of color detectionand the like. For example, color extraction and quantization of colorsmay cause image artifacts that can change the appearance of thecompressed image. Because the segmentation techniques in this disclosureare simplified, and a simple mask comprising opaque (white) areas isgenerated to overlay pixels in an image, additional processing tocombine and quantize colors is not required, thus resulting in lesssegmentation artifacts. FIGS. 9-11, for example, illustrate an exampleof using the method described herein to increase the image quality ofimages having pixels of several different colors.

In addition, the low level of segmentation processing as provided by thedescribed method may also be used in a color output device when set in atext mode. The method would assist in providing good image quality witha fast scanning alternative.

The method as described herein does not require complicatedsegmentation, and can operate on a band of an image, such as 16 lines ofan image. This capability allows the method or algorithm to have aminimal memory footprint. Thus, this disclosure provides a lower costalgorithm which may be implemented on a low memory platform. Forexample, unlike more complex or complicated segmentation basedalgorithms, such as those described in U.S. Pat. Nos. 7,242,802 and7,343,046 (noted above), the method described herein may utilize muchsimpler algorithms for MRC image processing and digital printing.

FIG. 7 illustrates an example of part of output image data that isproduced using known methods such as JPEG compression. As can be seen,the output image quality is degraded by the noise surrounding the edgesof the lines of the image or word (that is created during compression).For example, a large number of gray pixels of varying shades orintensities surround the letter “W,” thus creating the noise. Byoverlaying the mask with the image data as described in the presentdisclosure, the noise is masked out by the opaque areas of the mask, asshown in FIG. 8. Specifically, FIG. 8 illustrates output image data of aPDF image that is produced with JPEG compression of image data and anassociated mask. As can be seen, opaque areas of the mask overlay anumber of the gray pixels of varying shades that surrounded the letter“W” and others throughout the image. Thus, a higher quality output imageis produced.

As a second example, FIG. 9 illustrates part of output image data thatwas JPEG compressed to a size of 216939 bytes (note that FIG. 9 onlyshows a very small portion of a complete image). For example, the imageshown in FIG. 9 is a colored output image comprising substantially blueink or toner. Again, such compression may cause noise around the edgesof the lines. As shown in FIG. 9, upon compression, several pixels ofdifferent colors (e.g., yellow, green) surrounding the blue ink or tonerhave been output in the image data (e.g., along the side of the letter“B”). In FIG. 10, however, the image data has been compressed, stored,and/or output using the herein described technique, thus compressing theimage data to a PDF format with the mask technique. The image data asshown in FIG. 10 compresses the image to a size of 218649 bytes. As canbe seen, the image of FIG. 10 has improved image quality with comparablefile size. Thus, the neighboring pixels of different colors (e.g., notsubstantially of a blue ink or toner) are substantially hidden oroverlayed by the opaque (white) mask, producing an output image ofhigher quality. FIG. 11 illustrates the same image at a smaller filesize, having a reduced noise and very little degradation in imagequality. More specifically, the image data and mask of FIG. 11 is theprocessed image data with its associated mask which is compressed to asize of 118350 bytes.

FIG. 12 illustrates a block diagram of an image data processing system80 that may be used to compress image data and generate a mask inaccordance with an embodiment of the present disclosure. For example,the system 80 may comprise an input device 82, a compressor 86, aprocessor 88, memory 98 or storage 100, and an output device 104. Eachof the devices shown in system 80 may also be considered modules, and,therefore, the terms “device” and “module” are used interchangeablyherein. The input device 82 is configured to receive image data, such asgrayscale image data. The input device 82 may comprise any type ofdevice for receiving and/or inputting image data, such as a scanningdevice, facsimile device, computing device, copying device, MFD, etc. Inan embodiment, the input device 82 may comprise a conversion device 84for converting the received image data, such as converting grayscaleimage data from an input device color space, such as RGB (red, green,blue), to a processing color space, such as YCC, where intensity andcolor information are separated. Generally, the types of color spacesfor image data and the conversion of such color spaces is known in theart. The conversion device 84 may be a part of the input device 82 or aseparate device or module. At least one compressor 86 is provided insystem 80, and is configured to compress the image data received by theinput device 82.

A processor 88 is configured to process each pixel of the grayscaleimage data to identify pixels which may produce noise in an outputimage. A mask generator 90 configured to generate a mask associated withthe image data. In an embodiment, the processor 88 may comprise the maskgenerator 90, or, alternatively, the processor 88 may generate the mask.Additionally, in some embodiments, a segmentation module 94, acomparator device 96, and/or a threshold determination module 92 may beprovided in system 80. Segmentation module 94 may be used to generatethe mask plane 22, for example. Additionally and/or alternatively, acomparator device 96 may also be provided in system 80. In anotherembodiment, the segmentation module 94 may include more thoroughprocessing or examination of the image data. For example, in anembodiment, the segmentation module 94 may determine a rate of change ofthe pixels of image data, and identify pixels that are not changing fastenough (or sufficiently enough) and thus should not be included in theoutput image (i.e., and therefore associated with opaque areas of themask) even if they meet the intensity threshold criteria. In anembodiment, a simple version of a segmentation module 94 may becomparator device 96. Comparator device 96 may be used to compare theintensity of each of the pixels to a threshold, for example. Thresholdmodule 92 may be used to determine the threshold value for processingthe image data. For example, threshold module 92 may provide auser-activated threshold value or calculate a threshold value (e.g.,based on mean intensities of the pixels), or in some other manner. In anembodiment, mask generator 90 may comprise comparator device 96 and/orthreshold module 92 therein.

Memory 98 and/or storage 100 are storage devices for storing the imagedata and the mask. An output device 104 may be provided to output theimage data and the mask. Output device 104 may be any type of devicethat is designed to output the image data and the mask. For example, theoutput device may display, copy, print, or send the image data. Such anoutput device may be an MFD, printer, copier, scanner, facsimile device,display, a terminal, an image storage system, or CPU, for example. In anembodiment, the output device 104 may decompress the image data and themask before output. In an embodiment, a decompressor 102 is provided insystem 80 to decompress the image data and mask before sending thegrayscale image data and mask to output device 104. The decompressor 102and output device 104 may be the same module, or separate modules.

The processor 88 may execute machine readable executable instructionsstored in memory 98 or storage 100. For example, the method describedherein may be stored in the form of computer executable instructions soas to provide a computer readable media that may be executed by acomputer to direct a computer to perform the method to reduce noise inoutput image data.

It should be noted that each of the above devices or modules shown insystem 80 may be connected to, incorporated in, or communicate with anyof the other modules and/or modules not shown. The above noted devicesor modules of system 80 may also be separate and distinct. Also, thesystem and method may be used in different applications such as, forexample, devices capable of providing output via digital printing for amultitude of purposes, including, but not limited to, xerographicsystems, laser printing systems, ink jet printing systems, digitalcopiers and/or scanners, bookmaking machines, facsimile machines, andmulti-function machines.

While the principles of the disclosure have been made clear in theillustrative embodiments set forth above, it will be apparent to thoseskilled in the art that various modifications may be made to thestructure, arrangement, proportion, elements, materials, and componentsused in the practice of the disclosure.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Variouspresently unforeseen or unanticipated alternatives, modifications,variations, or improvements therein may be subsequently made by thoseskilled in the art which are also intended to be encompassed by thefollowing claims. The claims can encompass embodiments in hardware,software, or a combination thereof.

1. A method of reducing noise in output image data, the methodcomprising: receiving grayscale image data comprising a plurality ofpixels; processing each pixel to identify pixels which produce noise inan output image; generating a mask associated with the grayscale imagedata, the mask comprising information related to the pixels of the imagedata; compressing the image data and the mask, and storing thecompressed image data and the mask, wherein the mask assists inpreventing the identified pixels of the image data from being visiblewhen the image data is output, thereby reducing the noise in the outputimage.
 2. The method according to claim 1, the pixel being processed isidentified as a noise producing pixel.
 3. The method according to claim1, wherein the pixel being processed is identified as a pixel to bevisible when the image data is output, and the identified pixelscomprise pixels neighboring the pixel being processed.
 4. The methodaccording to claim 1, further comprising embedding or linking the maskwith the image data.
 5. The method according to claim 1, furthercomprising decompressing the image data and outputting the grayscaleimage data and the mask to an output device.
 6. The method according toclaim 1, wherein the processing of each pixel to identify pixels isperformed by comparing an intensity of each pixel to a threshold value.7. The method according to claim 6, wherein the mask comprisesinformation relating to pixels determined to have an intensity lowerthan the threshold value.
 8. The method according to claim 1, whereinthe processing of each pixel to identify pixels is performed bycomparing each pixel to its neighboring pixels.
 9. The method accordingto claim 1, wherein the method further comprises determining a thresholdvalue upon receiving the grayscale image data.
 10. A method according toclaim 9, wherein the method further comprises determining the intensityof each pixel of the grayscale image data, and determining the thresholdvalue based on the determined intensities.
 11. A method according toclaim 1, wherein the mask comprises pixel-by-pixel metadata.
 12. Amethod according to claim 1, wherein the mask comprises transparentregions and opaque regions, and wherein the transparent regions overlaythe pixels determined to be above the threshold value, and the opaqueregions overlay the pixels determined to be below the threshold valuewhen the image data is stored or output.
 13. A method according to claim1, wherein the image data and mask are stored using a page descriptionlanguage.
 14. An image data processing system comprising: an inputdevice configured to receive grayscale image data comprising a pluralityof pixels; a processor configured to process each pixel to identifypixels which produce noise in an output image; a mask generatorconfigured to generate a mask associated with the grayscale image data,the mask comprising information relating to the pixels of the imagedata; a compressor for compressing the image data and the mask, and astorage device for storing the image data and the mask, wherein the maskassists in preventing the identified pixels of the image data from beingvisible when the image data is output, thereby reducing the noise in theoutput image.
 15. The system according to claim 14, the pixel beingprocessed is identified as a noise producing pixel.
 16. The systemaccording to claim 14, wherein the pixel being processed is identifiedas a pixel to be visible when the image data is output, and theidentified pixels comprise pixels neighboring the pixel being processed.17. The system according to claim 14, wherein the mask is embedded orlinked with the image data.
 18. The system according to claim 14,further comprising an output device for decompressing and outputting theimage data and the mask.
 19. The system according to claim 14, whereinthe output device is selected from the group consisting of: a copier, aprinter, a scanner, a facsimile machine, and a multi-function device.20. The system according to claim 14, wherein the processor processeseach pixel by comparing an intensity of each pixel to a threshold value.21. The system according to claim 20, wherein the mask comprisesinformation relating to pixels determined to have an intensity lowerthan the threshold value.
 22. The system according to claim 14, whereinthe processor is configured to determine a threshold value uponreceiving the grayscale image data.
 23. The system according to claim22, wherein the processor is configured to determine the intensity ofeach pixel of the grayscale image data and determine the threshold valuebased on the determined intensities.
 24. The system according to claim14, wherein the processor is configured to identify pixels by comparingeach pixel to its neighboring pixels.
 25. The system according to claim14, wherein the mask comprises pixel-by-pixel metadata.
 26. The systemaccording to claim 14, wherein the mask comprises transparent regionsand opaque regions, and wherein the transparent regions overlay thepixels determined to be above the threshold value, and the opaqueregions overlay the pixels determined to be below the threshold valuewhen the image data is reproduced.